I started with the class structure: a Broker holding a map of topic to subscriber sets, a Subscriber interface with an onMessage callback, and Topic as a string wrapper.
Start by clarifying requirements and scale, then define the core abstractions (Topic, Subscriber, Broker) and the registration/delivery semantics. Walk through the data structures and concurrency model, and discuss trade-offs like synchronous vs asynchronous delivery and backpressure.
Pro tip: Emphasize thread-safety and lock granularity early, and mention how you'd handle a slow subscriber without blocking others—this shows production maturity beyond basic pub/sub.
Ask about expected scale, delivery guarantees (at-most-once, at-least-once), ordering, and whether subscribers can register/unregister dynamically. Confirm in-process constraints and performance goals.
Outline interfaces for Topic, Subscriber, and Broker with methods like subscribe, unsubscribe, publish, and a callback or queue for delivery. Specify that a subscriber can be registered to multiple topics.
Propose a thread-safe registry mapping topics to subscriber lists (e.g., ConcurrentHashMap with CopyOnWriteArrayList or fine-grained locks). Explain how to avoid blocking publishers and ensure safe concurrent access.
Decide between synchronous callbacks and asynchronous queues per subscriber. Discuss backpressure, slow subscribers, and error isolation so one failure doesn't affect others.
Compare design choices (e.g., lock-free vs locking, direct dispatch vs executor) and mention potential extensions like filtering, wildcards, or persistence if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's requirements and constraints, such as throughput, latency, and consistency needs. Then, propose a locking strategy that balances correctness and performance, explaining trade-offs and alternatives like lock-free structures. Finally, walk through a concrete example to illustrate your approach.
Pro tip: Demonstrate awareness of Amazon's leadership principles by emphasizing customer obsession (e.g., choosing a strategy that minimizes latency for end-users) and ownership (e.g., considering failure modes and operational simplicity).
Ask about expected throughput, latency sensitivity, consistency requirements, and whether publishers/subscribers are internal or external. This ensures your solution aligns with business needs.
Determine what data is shared between publishers and subscribers, such as a message queue or topic registry. This helps pinpoint where synchronization is needed.
Propose a locking approach (e.g., fine-grained locks, read-write locks, or lock-free structures) based on requirements. Explain why it fits and mention alternatives.
Discuss deadlocks, contention, and scalability. Explain how you'd mitigate them, such as lock ordering, backoff, or partitioning.
Walk through a concrete scenario, like a pub/sub system with a shared queue, showing how locks are acquired and released to ensure thread safety.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with at-most-once first since it's simpler: fire the callback, if it throws you log and move on.
Start by clarifying the delivery semantics your broker supports (at-most-once, at-least-once, exactly-once) and the trade-offs involved. Then explain the exception handling behavior, including retries, dead-letter queues, and idempotency, and tie it back to Amazon's customer-obsessed, ownership-driven culture.
Pro tip: Emphasize that you design for failure: assume callbacks will throw and build idempotent consumers with dead-letter queues and monitoring. This shows you think beyond the happy path and align with Amazon's operational excellence.
State the broker's delivery guarantee (e.g., at-least-once) and explain what that means for message duplication or loss. Mention how it's configured and any trade-offs.
Explain what happens when a subscriber callback throws: does the broker retry, nack, or drop the message? Detail retry policies, backoff, and max attempts.
Describe how failed messages are routed to a DLQ after retries, and how this prevents poison messages from blocking the queue.
Explain how consumers handle duplicates (idempotent processing) and whether ordering is preserved, especially with retries.
Mention monitoring, alerting, and metrics for failed deliveries, and how you'd debug and improve the system over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about per-subscriber bounded queues with a drop or block policy, and async dispatch through a thread pool so a slow consumer doesn't stall the publish call.
Start by clarifying the scenario and requirements (e.g., message delivery guarantees, subscriber types). Then discuss a layered strategy: backpressure, bounded queues, and flow control, while highlighting trade-offs between durability, latency, and resource usage. Conclude with monitoring and dynamic adjustments to handle edge cases.
Pro tip: Emphasize that the goal is not to avoid dropping messages but to make intentional, observable decisions about which messages to drop or delay, and to communicate those decisions to subscribers. This shows you understand real-world constraints and customer impact.
Ask about delivery guarantees (at-least-once, exactly-once), subscriber types (push vs pull), and acceptable latency. This sets the context for trade-offs.
Describe mechanisms like TCP flow control, pull-based consumption, or explicit credit-based flow control to let the broker signal the subscriber to slow down.
Explain how to set per-subscriber queue limits and define policies: drop oldest, drop newest, or spill to disk. Discuss the impact on message loss and ordering.
Mention metrics (queue depth, consumer lag) and automated actions like throttling, disconnecting slow subscribers, or scaling out. Highlight the need for alerts and dashboards.
Compare approaches: e.g., dropping messages vs. blocking producers vs. persisting to disk. Tie back to business requirements and Amazon leadership principles like Customer Obsession.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Swapping the subscriber set for a sorted structure keyed on priority.
Start by clarifying the requirements: what defines priority, how many levels, and whether strict ordering is needed. Then propose a design that separates messages into priority queues, using a broker or database with priority support, and discuss trade-offs like starvation and scalability.
Pro tip: Mention that Amazon often uses SQS with multiple queues per priority and a consumer that polls higher-priority queues first, but be prepared to discuss how to avoid starving low-priority messages.
Ask about priority levels, whether strict ordering is required, and the expected volume and latency. This ensures the design meets actual needs.
Decide how to store priority with each message, e.g., a priority field in the message metadata or separate queues per priority level.
Propose a system where consumers fetch from higher-priority queues first, or use a priority queue data structure. Discuss how to handle concurrency and ordering.
Discuss starvation of low-priority messages, fairness, scalability, and failure scenarios. Suggest mitigation like aging or weighted round-robin.
Recap the design, highlight how it meets requirements, and invite feedback or further questions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.